Injection drug use and food insecurity among HIV-hepatitis C virus co-infected individuals: associations, mechanisms, and interventions
Bibliographic record
Abstract
Background: In Canada, 20% of individuals living with HIV are estimated to be co-infected with hepatitis C virus (HCV). In addition to the high prevalence of injection drug use (IDU), the characteristics of individuals living with HIV-HCV co-infection reflect socioeconomic and sociodemographic vulnerability. Central to the concept of food insecurity (FI), a social determinant of health, is the focus on uncertain or inadequate food access due to limited financial resources. The existing evidence has documented high prevalences of FI, particularly severe FI, among individuals living with HIV. Furthermore, FI is associated with lower CD4 cell counts, incomplete HIV viral load suppression, and sub-optimal HIV treatment adherence. These consequences of FI motivate studies that focus on identifying modifiable risk factors for FI with the goal of informing interventions to reduce FI. However, given the differences between those living with HIV mono-infection and HIV-HCV co-infection and the context-specific nature of FI risk factors, the generalizability of findings from HIV-related studies that do not consider HCV co-infection is unclear. Therefore, novel research is needed to further our understanding of the relationship between IDU, a highly prevalent behaviour in this vulnerable subset of the HIV-positive population, and FI. Objectives: The overall aim of this doctoral thesis was to examine associations, mechanisms, and interventions related to IDU and FI, particularly severe FI, in a population of HIV-HCV co-infected individuals in Canada. Specifically, this dissertation addressed the following objectives using longitudinal cohort data from individuals living with HIV-HCV co-infection: 1. To examine the relationship between IDU and FI. 2. To examine whether unemployment is a mediator in the mechanism linking IDU and severe FI. 3. To examine whether a substance use intervention, methadone maintenance treatment, is associated with a lower risk of severe FI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".